检索结果-内蒙古大学图书馆

arXiv 2024年

作者： Peng, Xin Wang, Shangwen Qin, Yihao Lin, Bo Chen, Liqian Mao, Xiaoguang Cheng, Jieren The Key Laboratory of Software Engineering for Complex Systems College of Computer Science and Technology National University of Defense Technology Changsha China The Hainan University Haikou China

software vulnerability detection is crucial for high-quality software development. Recently, some studies utilizing Graph Neural Networks (GNNs) to learn the graph representation of code in vulnerability detection tasks have achieved remarkable success. However, existing graph-based approaches mainly face two limitations that prevent them from generalizing well to large code graphs: (1) the interference of noise information in the code graph;(2) the difficulty in capturing long-distance dependencies within the graph. To mitigate these problems, we propose a novel vulnerability detection method, ANGEL, whose novelty mainly embodies the hierarchical graph refinement and context-aware graph representation learning. The former hierarchically filters redundant information in the code graph, thereby reducing the size of the graph, while the latter collaboratively employs the Graph Transformer and GNN to learn code graph representations from both the global and local perspectives, thus capturing long-distance dependencies. Extensive experiments demonstrate promising results on three widely used benchmark datasets: our method significantly outperforms several other baselines in terms of the accuracy and F1 score. Particularly, in large code graphs, ANGEL achieves an improvement in accuracy of 34.27%-161.93% compared to the state-of-the-art method, AMPLE. Such results demonstrate the effectiveness of ANGEL in vulnerability detection tasks. Copyright © 2024, The Authors. All rights reserved.

关键词： Graph neural networks

来源：评论

学校读者我要写书评

暂无评论

Texture and Orientation-based Feature Extraction for Robust Facial Expression Recognition 21

Texture and Orientation-based Feature Extraction for Robust ...

引用

21st IEEE/ACIS International Conference on software engineering Research, Management and Applications, SERA 2023

作者： Khan, Sajid Ali Bin Abdulrahman Alawairdhi, Mohammed Al-Akhras, Mousa Foundation University Islamabad Department of Software Engineering Islamabad Pakistan Saudi Electronic University College of Computing and Informatics Saudi Arabia The University of Jordan Amman King Abdullah II School for Information Technology Computer Information Systems Department Jordan

ISBN: (纸本)9798350345889

Facial expressions are the most effective way to characterize people's motives, emotions, and feelings. Several new methods are proposed each year;however, the accuracy of facial expression recognition still needs to be improved especially in uncontrolled conditions. In this paper, we propose a hybrid facial expression model that considers both texture and orientation features to classify expressions. Two types of descriptors namely Local binary pattern and Weber local descriptor are used to preserve the local intensity information and orientation of edges. In the next step, computing the Histograms of oriented gradients (HOG) features from the Local binary pattern and Weber local descriptor images to capture micro-expressions. Then, the AdaBoost feature selection algorithm is utilized to choose the best features from the combined HOG features. The results of the experiments demonstrate that the method proposed in this study performs better than existing methods. © 2023 IEEE.

关键词： Local binary pattern

来源：评论

学校读者我要写书评

暂无评论

State-based Sandbox Tool for Distributed Malware Detection with Avoid Techniques 13

State-based Sandbox Tool for Distributed Malware Detection w...

引用

13th International Conference on Dependable systems, Services and Technologies, DESSERT 2023

作者： Rehida, Pavlo Markowsky, George Sachenko, Anatoliy Savenko, Oleg Khmelnytskyi National University Department of Computer Engineering and Information Systems Khmelnytskyi Ukraine Kennesaw State University Department of Software Engineering and Game Development Marietta United States Kazimierz Pulaski University of Technology and Humanities Department of Informatics Radom Poland Research Institute for Intelligent Computer Systems West Ukrainian National Unversity Ternopil Ukraine

ISBN: (纸本)9798350396119

This paper deals with the problem of detecting the malware by using emulation approach. Modern malware include various avoid techniques, to hide its anomaly actions. Advantages of using sandbox and emulation technologies are described. Various anti-emulation techniques that are used in modern malware considered. Obfuscation as one primary approach to hide malware malicious actions described and discussed. State of emulator is presented, and the advantages of its usage are covered. Distributed model for malware detection is considered. Basic emulator and its current capabilities presented. Prepared files that represent malware are described. Experimental results for developed files that differs with included avoid techniques are presented. Disadvantages of proposed approach is described. Future research and sandbox improvement are described. © 2023 IEEE.

关键词： anti-emulation techniques malware detection obfuscation techniques sandbox

来源：评论

学校读者我要写书评

暂无评论

Applications of Digital Twins for Demand Side Recommendation Scheme with Consumer Comfort Constraints

Applications of Digital Twins for Demand Side Recommendation...

引用

2023 IEEE PES Innovative Smart Grid Technologies Europe, ISGT EUROPE 2023

作者： Onile, Abiodun E. Belikov, Juri Petlenkov, Eduard Levron, Yoash Department of Software Science Tallinn University of Technology Akadeemia tee 15a Tallinn12618 Estonia Department of Computer Systems Tallinn University of Technology Akadeemia tee 15a Tallinn12618 Estonia Faculty of Electrical and Computer Engineering Technion—Israel Institute of Technology Haifa3200003 Israel

ISBN: (纸本)9798350396782

The next evolution of traditional energy systems towards smart grid will require end-consumers to actively participate and make informed decisions regarding their energy usage. Industry 4.0 facilitates such progress by allowing more advanced analytics and creating means for end-consumers and distributed grid assets to be modelled as their Digital twins (DT) equivalents, paving the way for asset-level analytics. Note-worthily, consumers’ comfort is crucial towards promotion of easy adoption of such models from consumers’ perspectives. This study presents the application of hybrid DT and multiagent reinforcement learning models for real-time estimation of end-consumers future energy behaviors while generating actionable recommendation feedback for improving their energy efficiency and enhancing end-user comfort. © 2023 IEEE European Union.

关键词： Consumer comfort Demand side recommender system Distributed power systems Hybrid digital twins Industry 5.0

来源：评论

学校读者我要写书评

暂无评论

UR4NNV: Neural Network Verification, Under-approximation Reachability Works!

arXiv

引用

arXiv 2024年

作者： Liang, Zhen Wu, Taoran Zhao, Ran Xue, Bai Wang, Ji Yang, Wenjing Deng, Shaojun Liu, Wanwei College of Computer Science and Technology National University of Defense Technology China Institute of Software Chinese Academy of Sciences China School of Computer Science and Technology University of Chinese Academy of Sciences China Key Laboratory of Software Engineering for Complex Systems National University of Defense Technology China

Recently, formal verification of deep neural networks (DNNs) has garnered considerable attention, and over-approximation based methods have become popular due to their effectiveness and efficiency. However, these strategies face challenges in addressing the "unknown dilemma" concerning whether the exact output region or the introduced approximation error violates the property in question. To address this, this paper introduces the UR4NNV verification framework, which utilizes under-approximation reachability analysis for DNN verification for the first time. UR4NNV focuses on DNNs with Rectified Linear Unit (ReLU) activations and employs a binary tree branch-based under-approximation algorithm. In each epoch, UR4NNV under-approximates a sub-polytope of the reachable set and verifies this polytope against the given property. Through a trial-and-error approach, UR4NNV effectively falsifies DNN properties while providing confidence levels when reaching verification epoch bounds and failing falsifying properties. Experimental comparisons with existing verification methods demonstrate the effectiveness and efficiency of UR4NNV, significantly reducing the impact of the "unknown dilemma".MSC Codes 68Q60, 68T07 Copyright © 2024, The Authors. All rights reserved.

关键词： Deep neural networks

来源：评论

学校读者我要写书评

暂无评论

Architecting Digital Twins for Intelligent Transportation systems

Architecting Digital Twins for Intelligent Transportation Sy...

引用

IEEE International Conference on software Architecture Workshops (ICSAW)

作者： Hiya Bhatt Sahil Karthik Vaidhyanathan Rahul Biju Deepak Gangadharan Ramona Trestian Purav Shah Software Engineering Research Centre IIIT Hyderabad India Computer Systems Group IIIT Hyderabad India Faculty of Science and Technology Middlesex University London

ISBN: (数字)9798331533366

ISBN: (纸本)9798331533373

Modern transportation systems face growing challenges in managing traffic flow, ensuring safety, and maintaining operational efficiency amid dynamic traffic patterns. Addressing these challenges requires intelligent solutions capable of real-time monitoring, predictive analytics, and adaptive control. This paper proposes an architecture for DigIT, a Digital Twin (DT) platform for Intelligent Transportation systems (ITS), designed to overcome the limitations of existing frameworks by offering a modular and scalable solution for traffic management. Built on a Domain Concept Model (DCM), the architecture systematically models key ITS components enabling seamless integration of predictive modeling and simulations. The architecture leverages machine learning models to forecast traffic patterns based on historical and real-time data. To adapt to evolving traffic patterns, the architecture incorporates adaptive Machine Learning Operations (MLOps), automating the deployment and lifecycle management of predictive models. Evaluation results highlight the effectiveness of the architecture in delivering accurate predictions and computational efficiency.

关键词： Adaptation models software architecture Transportation Computer architecture Predictive models Traffic control Real-time systems Digital twins Safety Predictive analytics

来源：评论

学校读者我要写书评

暂无评论

advanced Multi-Spectral Image Processing Techniques for Enhanced Remote Sensing and Comprehensive Environmental Monitoring in Diverse Ecosystems

Advanced Multi-Spectral Image Processing Techniques for Enha...

引用

2024 IEEE International Conference on Recent Advances in Science and engineering technology, ICRASET 2024

作者： Jagannathan, J. Ponmalar, A. Pandiarajan, Rajkumar Sudha, I. Ramesh, P.S. Vellore Institute of Technology School of Computer Science Engineering and Information Systems Department of Software and Systems Engineering Katpadi Vellore India R.M.K. Engineering College Tamilnadu Chennai India School of Computing Kalasalingam Academy Of Research and Education Department of Information and Technology Krishnankoil Virudhunagar India Saveetha Institute of Medical and Technical Sciences Simats University Saveetha School of Engineering Department of Computer Science and Engineering Tamil Nadu Chennai India Vel Tech Rangarajan Dr.Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Tamilnadu Chennai India

ISBN: (纸本)9798350388602

The research is aimed at improvement of multi-spectral image analysis for remote sensing and monitoring the environment at various ecosystems. A combined spectral imagery encompasses several spectrums at a time compared to single spectral imagery that takes only one spectrum at an instance or at a given area to cover all these spectrums. The study employs digital high resolution satellite images and ground based data to quantify the vegetation vigour, water quality, soil characteristics and changes in land cover. Features, indexes, and classification routines are employed to process the data that is collected with the aid of avionics and other peripherals. This provincial level modelling highlights important gains in environmental status, which includes the general improvement in vegetation condition, water quality as well as physical properties of the soil in the province. This study focuses on the complementary possibilities of integration of multi-spectral imaging with other remote sensing technologies and the high performance of data processing. These techniques play significant roles if used to give current and comprehensive information in the management and conservation of the environment as highlighted in the study. © 2024 IEEE.

关键词： data analysis environmental monitoring image processing techniques machine learning Multi-spectral imaging remote sensing soil properties vegetation health water quality

来源：评论

学校读者我要写书评

暂无评论

advanced Machine Learning Algorithms for 1Predictive Analytics in Healthcare to Enhance Patient Outcomes with Data-Driven Insights

Advanced Machine Learning Algorithms for 1Predictive Analyti...

引用

2024 IEEE International Conference on Recent Advances in Science and engineering technology, ICRASET 2024

作者： Pandiarajan, Rajkumar Jagannathan, J. Ramesh, P.S. Ponmalar, A. Sudha School of Computing Kalasalingam Academy Of Research and Education Department of Information and Technology Krishnankoil Virudhunagar India Vellore Institute of Technology School of Computer Science Engineering and Information Systems Department of Software and Systems Engineering Katpadi Vellore India Vel Tech Rangarajan Dr.Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Tamilnadu Chennai India R.M.K. Engineering College Tamilnadu Chennai India Saveetha Institute of Medical and Technical Sciences Simats University Saveetha School of Engineering Department of Computer Science and Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350388602

This research aims to explore the use of modern complex defensive machine learning algorithms in the provision of predictive analytics for health improvement. Incorporating electronic health records, medical image information, and genomic data the work proposes and assesses multiple machine learning models including deep learning, ensemble learning, and reinforcement learning. The main findings shown very high performance in disease prediction, in chronic illness and in the application of efficiency improvements in hospitals as a whole. Overall, such threats as data quality, data privacy, and model interpretability have been discussed in the research and emphasize the importance of the machine learning approach in healthcare. It is highlighted that further development and integration of the interdisciplinary approaches as well as the use of the transparent and interpretable artificial intelligence and machine learning models can be urgent to increase the acceptance among the healthcare staff. This paper enhances the literature on applying machine learning in healthcare systems adding to the repertoire of knowledge for improved patient care. © 2024 IEEE.

关键词： Electronic health record

来源：评论

学校读者我要写书评

暂无评论

Development of Microcontroller-Based Pure Sine Wave Inverter with Overload and Overheating Protection 17

Development of Microcontroller-Based Pure Sine Wave Inverter...

引用

17th IEEE International Conference on advanced Trends in Radioelectronics, Telecommunications and Computer engineering, TCSET 2024

作者： Holovatyy, Andriy Lobur, Mykhaylo Holovata, Sofia Lukaszewicz, Andrzej Nowakowski, Marek Kolesnyk, Kostyantyn Lviv Polytechnic National University Department of Computer-Aided Design Systems Lviv Ukraine Ukrainian National Forestry University Department of Software Engineering Lviv Ukraine Bialystok University of Technology Department of Machinery Design and Operation Bialystok Poland Military Institute of Armoured and Automotive Technology Development Department Sulejowek Poland Lviv Polytechnic National University Department of Computera-Aided Design Systems Lviv Ukraine

ISBN: (纸本)9798331520564

In the paper, hardware and software of the Arduino based pure sine wave inverter with overload and overheating protection have been developed. The inverter constantly monitors the consumption current, the battery voltage, the temperature of the power switches (radiator) and, if their parameters are normal, forms a sinusoidal voltage of 230 V, 50 Hz at the output of the inverter, similar to the network voltage. The inverter provides the display of information on the LCD screen about current and power consumption, battery voltage, temperature of the inverter power switches, as well as display of emergency icons when the permissible operating parameters are exceeded. The inverter generates sound and visual warning messages with LED indicators and buzzer. The inverter can be used as a lightweight low-power 100 W network inverter with sine-wave voltage for autonomous power supply of low-power electronics. © 2024 IEEE.

关键词： Microcontrollers

来源：评论

学校读者我要写书评

暂无评论

Leveraging Digital Twins and Demand Side Recommender Chatbot for Optimizing Smart Grid Energy Efficiency

Leveraging Digital Twins and Demand Side Recommender Chatbot...

引用

2023 IEEE PES Innovative Smart Grid Technologies - Asia, ISGT Asia 2023

作者： Onile, Abiodun E. Belikov, Juri Petlenkov, Eduard Levron, Yoash Tallinn University of Technology Department of Software Science Akadeemia tee 15a Tallinn12618 Estonia Tallinn University of Technology Department of Computer Systems Akadeemia tee 15a Tallinn12618 Estonia Technion - Israel Institute of Technology Faculty of Electrical & Computer Engineering Haifa3200003 Israel

ISBN: (纸本)9798350327748

Electricity consumers often face the challenge of selecting an optimal plan for saving energy. Strategic energy management and monitoring plays a key role in overcoming these challenges. Developments around Industry 5.0 powered smart grid proffers adequate solutions which allows end-consumers to monitor their energy performance towards effecting demand side recommendation services. Specific problems where end-users are likely to ignore recommended advice exists, thereby contributing to widening 'knowledge-action gap'. An ensemble of hybrid digital twins (DT) asset modelling based on ordinary differential equation (ODE) physics engine and data driven recurrent neural network (RNN) prediction approach alongside PageRank based asset behavior scoring algorithm deployed for demand side recommender and generative pre-trained transformers (GPT) based conversational chatbot technology show effectiveness in engaging and extending end-consumers interests in recommended advice. The novelty of the study lies in extending current scope of demand side recommender scheme via conversational chatbot interface for DT of electricity grid assets that better engage and monitors end-user's energy behavior while offering appropriate energy efficiency advice towards achieving energy conservation goals of smart grid consumers. Extensive experiments, including evaluation of end-user studies, revealed the effectiveness of proposed approach in terms of improved recommendation quality and user engagement towards net electricity demand reduction. © 2023 IEEE.

关键词： Energy efficiency

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：